{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selected Exercises for `numpy`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看numpy的版本号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.17.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建一个10*10的矩阵，其元素全是1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.ones((10, 10))\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建一个3*3的矩阵，元素分别为0到8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(0, 9, 1)\n",
    "b.reshape((3, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检查下面这个矩阵x元素的最大值和最小值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.random((10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9920430767265134, 0.0016795480651145)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.max(), x.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的矩阵x是5 * 5的、元素全是1的矩阵，如何给它加一个宽度为1的边框，使得新矩阵是7*7，而且边缘一圈的值为0？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.ones((5, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = np.zeros((1, 5))\n",
    "y = np.concatenate([m, x, m], axis=0)\n",
    "n = np.zeros((7, 1))\n",
    "np.concatenate([n, y, n], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Better Answer\n",
    "np.pad(x, pad_width=1, constant_values=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建一个5 * 5 的矩阵，以随机数进行填充。并把这个矩阵进行标准化（即减去平均值再除以标准差）。标准化之后的标准差是多少？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array = np.random.rand(5, 5)\n",
    "((array - array.mean()) / array.std()).std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把数组x中值大于3小于8的数都添一个负号，再打印出来检查看看对不对"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -1,  -2,  -3,  -4,  -5,  -6,  -7,  -8,  -9, -10, -11])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3 -4 -5 -6 -7  8  9 10]\n"
     ]
    }
   ],
   "source": [
    "x[(x > 3) & (x < 8)] *= -1\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如何把1.34353保留两位小数？如何只保留整数？如何四舍五入保留？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.34, 1.0)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(1.34353, decimals=2), np.around(1.34353) #负数我没有懂是什么意思"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1213200.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.around(1213163.34353, decimals=-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.34, 2, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Answer\n",
    "round(1.34353, 2), round(1.34353), int(1.34353)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用`np.intersect1d`函数，试问下列两个array有哪些共同元素？分别在什么位置？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2, 4]), array([1, 3]), array([0, 1]))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.intersect1d([1, 2, 3, 4, 5], [2, 4, 6], return_indices=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "今天几号？可以这么问numpy哈哈哈哈"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2020-02-12')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('today')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把下面这个array首位颠倒一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4, 67, 50, 77, 86, 95,  1, 30, 90, 61])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.randint(low=0, high=100, size=(10))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x.reverse() 为啥用不了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([61, 90, 30,  1, 95, 86, 77, 50, 67,  4])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Answer\n",
    "x[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对下面这个array从大到小排序，然后打印看看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[58, 37, 58, 30, 23, 82, 55, 24, 29, 16]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.randint(low=0, high=100, size=(10)).reshape(1, -1)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(np.ndarray.sort(x, axis=1))   #aaaaaa怎么回事弄不出来"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9, 12, 12, 13, 22, 52, 78, 91, 94, 98]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([95, 73, 72, 52, 52, 41, 39, 37, 32,  3])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Answer\n",
    "y = np.sort(x)[0]\n",
    "y[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "X和Y是平面直角坐标系上点的坐标，请把这些点的坐标转化为极坐标。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = np.random.random((10,2))\n",
    "X, Y = Z[:,0], Z[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arctan( Y / X ) / 180 * np.pi\n",
    "y = np.sqrt( X ** 2 + Y ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Answer\n",
    "x = np.arctan( Y / X ) * 180 / np.pi ## rad to deg: np.rad2deg()\n",
    "y = np.sqrt( X ** 2 + Y ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "搞明白这句话是在干什么"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.043578811837319154, 0.9142286611628676),\n",
       " (0.9541775743197939, 0.4805559340290706),\n",
       " (0.9679955495611694, 0.6715210204904385),\n",
       " (0.09333279477622702, 0.6606533505988238),\n",
       " (0.9316625360555475, 0.15288010581115075),\n",
       " (0.1012452928355263, 0.8000594896803639),\n",
       " (0.22206531093549498, 0.5428681841827278),\n",
       " (0.16813193392258574, 0.6588795525184619),\n",
       " (0.3373967530178904, 0.5434741533156571),\n",
       " (0.8012858977845003, 0.5528798154659287)]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X, Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "#zip(X, Y)把X, Y变成zip对象，再用list变成列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### 咋跟没说一样咧"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把Z最大值的位置处的元素换为-99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = np.random.random(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z[np.argmax(Z)] = -99"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "找出下面这个array中哪个元素最接近15？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = np.random.randint(low=0, high=25, size=(30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(Z - 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "搞明白下面这段是在干甚"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, Y = np.meshgrid(np.linspace(-1,1,10), np.linspace(-1,1,10))\n",
    "D = np.sqrt(X * X + Y * Y)\n",
    "sigma, mu = 1.0, 0.0\n",
    "G = np.exp(-((D - mu)**2 / (2.0 * sigma**2)))\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(G)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "#坐标为（X, Y）的点距离原点的距离是以0为均值，以1为标准差的正态分布（公式前的根号下2pi呢？）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 看的是color 不是距离 OwO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请用np.load函数加载储存在`array-with-nan.npy`文件中的numpy矩阵，并用图像观察之。\n",
    "\n",
    "1) 白色的点是啥子？\n",
    "\n",
    "2) 检查一下矩阵里是否存在`np.nan` (Not A Number). 如果存在，这些nan在何处？index分别是什么？\n",
    "\n",
    "3) 将这些nan都换成9，再画出图来看看（记得画出colorbar），白点变成啥子了？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "S = np.load('array-with-nan.npy')\n",
    "plt.imshow(S)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1) nan 'not a number'没有数字，也就没有颜色可以代表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  3,  3,  5,  5,  7, 11, 14, 14, 14, 16, 20, 21, 25, 27, 28,\n",
       "        28, 31, 31, 32, 35, 35, 36, 37, 39, 40, 46, 48, 51, 54, 56, 57, 57,\n",
       "        61, 62, 62, 65, 66, 69, 70, 75, 77, 78, 81, 82, 86, 92, 92, 97]),\n",
       " array([99, 51, 45, 90, 67, 89, 30,  7, 16, 64, 75, 18, 39, 94, 25, 13, 40,\n",
       "        57, 52, 64, 23, 46, 66, 53,  4, 30, 61, 60, 28, 67, 30, 35,  1, 14,\n",
       "        97, 46, 89, 10, 49, 66, 44, 20, 89, 70, 63, 21, 31, 42, 93, 69]))"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(np.isnan(S))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "S[np.where(np.isnan(S))] = 9\n",
    "plt.imshow(S)\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "# 3) 变成了✨"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如何把变量x = True变成False？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logical_not(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Answer\n",
    "not True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
